This package provides a strong type system for R which supports symbol declaration and assignment with type checking and condition checking.

Regularized Greedy Forest wrapper of the Regularized Greedy Forest <https://github.com/RGF-team/rgf/tree/master/python-package> python package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.

Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve. Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>. David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.

Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.

Selects one model with variable selection FDR controlled at a specified level. A q-value for each potential variable is also returned. The input, variable selection counts over many bootstraps for several levels of penalization, is modeled as coming from a beta-binomial mixture distribution.

An R interface to the Chemistry Development Kit, a Java library for chemoinformatics. Given the size of the library itself, this package is not expected to change very frequently. To make use of the CDK within R, it is suggested that you use the rcdk package. Note that it is possible to directly interact with the CDK using rJava'. However rcdk exposes functionality in a more idiomatic way. The CDK library itself is released as LGPL and the sources can be obtained from <https://github.com/cdk/cdk>.

Generates random walks of various types by providing a set of functions that are compatible with the tidyverse'. The functions provided in the package make it simple to create random walks with a variety of properties, such as how many simulations to run, how many steps to take, and the distribution of random walk itself.

These tools implement in R a fundamental part of the software PACTA (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (<https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.

This package uses either the statconnDCOM server (via the rcom package) or the RDCOMClient to communicate with MS-Word via the COM interface.

Much as roxygen2 allows one to document functions in the same file as the function itself, roxut allows one to write the unit tests in the same file as the function. Once processed, the unit tests are moved to the appropriate directory. Currently supports testthat and tinytest frameworks. The roxygen2 package provides much of the infrastructure.

This package provides functions and datasets required for the ST 370 course at North Carolina State University.

Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time.

Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 <doi:10.1371/journal.pgen.1009923>). Ravages also enables to perform genetic simulations (Bocher et al. 2023 <doi:10.1002/gepi.22529>).

Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. RolWinWavCor also plots at the same graphic the time series under study. The RolWinWavCor was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in RolWinWavCor are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in RolWinWavCor'. Methods derived from Polanco-Martà nez et al (2018) <doi:10.1016/j.physa.2017.08.065>).

Multivariate regression methodologies including classical reduced-rank regression (RRR) studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>, reduced-rank regression via adaptive nuclear norm penalization proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>, robust reduced-rank regression (R4) proposed by She and Chen (2017) <doi:10.1093/biomet/asx032>, generalized/mixed-response reduced-rank regression (mRRR) proposed by Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>, row-sparse reduced-rank regression (SRRR) proposed by Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>, reduced-rank regression with a sparse singular value decomposition (RSSVD) proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x> and sparse and orthogonal factor regression (SOFAR) proposed by Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.

Designed to create and display complex tables with R, the rtables R package allows cells in an rtables object to contain any high-dimensional data structure, which can then be displayed with cell-specific formatting instructions. Additionally, the rtables.officer package supports export formats related to the Microsoft Office software suite, including Microsoft Word ('docx') and Microsoft PowerPoint ('pptx').

Researchers across disciplines often face biased regression model estimates due to endogenous regressors correlated with the error term. Traditional solutions require instrumental variables (IVs), which are often difficult to find and validate. This package provides flexible, alternative IV-free methods using copulas, as described in the practical guide to endogeneity correction using copulas (Yi Qian, Tony Koschmann, and Hui Xie 2025) <doi:10.1177/00222429251410844>. The current version implements the two-stage copula endogeneity correction (2sCOPE) method to fit models with continuous endogenous regressors and both continuous and discrete exogenous regressors, as described in Fan Yang, Yi Qian, and Hui Xie (2024) <doi:10.1177/00222437241296453>. Using this method, users can address regressor endogeneity problems in nonexperimental data without requiring IVs.

An R Interface to EPP-lab v1.0. EPP-lab is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.

Inspired by the classic RSA', we developed the improved Generalized Reporter Score-based Analysis (GRSA) method, implemented in the R package ReporterScore', along with comprehensive visualization methods and pathway databases. GRSA is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the GRSA supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.

The Linear Programming via Regularized Least Squares (LPPinv) is a two-stage estimation method that reformulates linear programs as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, LPPinv solves linear inequality, equality, and bound constraints by (1) constructing a canonical constraint system and computing a pseudoinverse projection, followed by (2) a convex-programming correction stage to refine the solution under additional regularization (e.g., Lasso, Ridge, or Elastic Net). LPPinv is intended for underdetermined and ill-posed linear problems, for which standard solvers fail.

Tests linear regressions for significance reversal through leave-one(multiple)-out.

R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.